Find Academic Supervisors

Carlos MastalliView profile

Assistant Professor

• Legged Robotics
• Optimal Control
• Motion Planning
• +4 more

Carlos Mastalli is an Assistant Professor at Heriot-Watt University (UK) and a Research Scientist at the Florida Institute for Human & Machine Cognition (USA). He leads the Robot Motor Intelligence (RoMI) Lab, focusing on legged robotics, optimal control, and numerical optimization. His work has been conducted in collaboration with leading institutions including IIT (Italy), LAAS-CNRS (France), ETH Zürich (Switzerland), and the University of Edinburgh (UK). He is a core developer of the Crocoddyl optimal control library, fostering open science and community-driven research. Research interests include: Legged Robotics Optimal Control and Trajectory Optimization Multi-Contact Dynamics and Planning Data-Driven Modeling for Robotics Autonomous Navigation in Dynamic Environments He manages advanced robotics equipment such as the B1, Go1, and Z1 Pro robots, and supports PhD students in projects involving legged robotics and optimization. Mastalli's recent publications emphasize real-time control algorithms, inertial estimation, and perceptive locomotion strategies for legged systems.

• Blind Signal Processing
• Bayesian Estimation
• Particle Filters
• +3 more

Ayşın Baytan Ertüzün is a Professor at Boğaziçi University, specializing in signal processing and statistical methodologies. Their research focuses on blind signal processing, Bayesian estimation techniques, particle filters, and seismological applications. They have contributed extensively to defect detection in textiles using advanced signal processing methods like wavelet transforms and independent component analysis, as well as geophysical signal analysis through Bayesian deconvolution approaches. Over 25 years of academic work includes pioneering work in non-Gaussian processes, time-varying autoregressive models, and particle filtering algorithms for complex signal environments. Their research bridges theoretical advancements with practical applications in manufacturing quality control and seismic data analysis. Key technical contributions include hybrid deconvolution methods for Bernoulli-Gaussian processes, subband domain subspace analysis for texture defects, and entropy-based 2D lattice filter structures. Their work often emphasizes robust statistical frameworks for non-stationary and non-Gaussian signal environments, with applications spanning from environmental forecasting (wind speed modeling) to medical signal analysis (respiratory sound coherence). Publications span from foundational 1990s lattice filter innovations to modern Bayesian approaches. Their methodologies are characterized by interdisciplinary fusion of signal processing theory with real-world industrial and geophysical challenges. No specific awards are listed in the provided materials, though their extensive publication record indicates sustained academic impact.

• Control Systems
• Marine Robotics
• Autonomous Systems
• +3 more

Henrik Schmidt-Didlaukies is a Research Fellow in the Department of Engineering Cybernetics at NTNU. His work focuses on advanced control systems for marine and robotic systems, with a particular emphasis on path following, navigation, and robust control methodologies. He has contributed to projects involving underwater vehicles, autonomous marine systems, and sensor-based control strategies. His research integrates theoretical frameworks with practical applications, including experiments in confined maritime environments and cooperative robotic systems. Key research areas include: Nonlinear control and adaptive systems Path planning and guidance algorithms Underwater robotics and vehicle dynamics Autonomous navigation and sensor fusion Collision avoidance and safety-critical systems Recent work highlights include developing anti-windup guarantees for curved path guidance, learning-based optimization for marine vehicles, and experimental validation of obstacle-aware collision avoidance. He has presented findings at major conferences including IFAC World Congress and IEEE OES events.

Radoslav PaulenView profile

Associate Professor

• Control Systems
• Chemical Engineering
• Nonlinear Dynamics
• +5 more

Radoslav Paulen is an Associate Professor and Deputy Head of the Department of Informatization and Process Management at the Slovak University of Technology in Bratislava, Faculty of Chemical and Food Technology. He holds a PhD in Chemical Engineering from the same institution. His research focuses on estimation and optimal control of nonlinear dynamic systems, particularly in chemical and biochemical industries. Key areas include guaranteed and statistical system identification, dynamic optimization, and predictive control. Education: PhD in Chemical Engineering (2012) Master's in Engineering (2008) Bachelor's degree (2006) Research Interests: Paulen's work emphasizes robust methodologies for system modeling and control, with applications in industrial processes. His group develops techniques for state and parameter estimation, global optimization, and model predictive control. Notable contributions include ellipsoidal ensemble methods for set-membership estimation and data-driven inferential sensor design for petrochemical processes. Awards: 2020 Best Article Award in Journal of Process Control 2018 Marie Curie Fellowship 2017 STU Scientist of the Year Advising & Collaboration: Paulen supervises a research team of 18 active and past students. Collaborations include Imperial College London, TU Dortmund, ShanghaiTech University, and industrial partners like Slovnaft and Siemens. His work bridges academic research with industrial applications, focusing on real-time optimization and control strategies for membrane separation, distillation columns, and polymerization reactors. Labs & Teams: Active in the Institute of Informatics, Automation and Mathematics, leading projects on process informatics and model-based control systems.

Fatemeh GhoreishiView profile

Assistant Professor

• Machine Learning
• Bayesian Statistics
• Autonomous Systems
• +4 more

Fatemeh Ghoreishi is an Assistant Professor at Northeastern University, holding joint appointments in the Department of Civil and Environmental Engineering and the Khoury College of Computer Sciences . Her research focuses on machine learning, Bayesian statistics, and decision-making under uncertainty, with applications in autonomous systems, transportation, and cyber-physical systems. She leads the Institute for Experiential AI and collaborates across disciplines to advance AI-driven solutions for complex systems. Education: Ph.D./M.Sc., Mechanical Engineering (Minor in Applied Statistics), Texas A&M University (2019/2016) M.Sc., Biomedical Engineering, Iran University of Science & Technology (2014) B.Sc., Mechanical Engineering, University of Tehran (2012) Research Interests: Dr. Ghoreishi develops data-driven methodologies for uncertainty quantification, reinforcement learning for autonomous systems, and human-AI collaboration. Her work integrates Bayesian inference, optimization, and sensor data to solve real-world challenges in transportation, robotics, and infrastructure security. Recent projects include AI for disaster response, privacy-preserving federated learning, and safe control of autonomous vehicles. Awards & Grants: $900,000 U.S. Army grant (2024): 'Interactive Human-Agent Teaming Via Multiple Feedback Types' $60,000 U.S. Army grant (2023): 'BRITE: Bayesian Inference and Preference Learning' IEEE Senior Member (2025) 2025 AIAA SciTech Best Paper Finalist 2024 MECC Conference Best Paper Award Advising & Teams: She mentors Ph.D. students (e.g., Negar Asadi) and undergraduates (e.g., Ise Okhiria, Alyssa Lee). Her lab emphasizes interdisciplinary collaboration, with affiliations at Northeastern’s Institute for Experiential AI and partnerships with the University of Utah and University of Michigan.

Soner AydınlıkView profile

Assistant Professor

• Numerical Analysis
• Computational Mathematics
• Fractional Calculus
• +4 more

Soner Aydınlık serves as an Assistant Professor in the Department of Mathematics Engineering at Istanbul Technical University, specializing in advanced computational methodologies for engineering and physical science applications. His research bridges theoretical mathematics with practical engineering challenges through innovative numerical techniques. His core research focuses on Numerical Analysis and Computational Mathematics , with particular expertise in Fractional Calculus applications, Thermoelasticity modeling, and Vibration Analysis of structural systems. Aydınlık has pioneered computational strategies involving Chebyshev polynomials and finite difference methods to solve complex nonlinear problems in fluid dynamics, electrohydrodynamics, and environmental systems. His work demonstrates exceptional precision in modeling phenomena like electrohydrodynamic flows, fractional-order differential equations, and three-dimensional plate dynamics under thermal stresses. Analysis of his 2022-2025 publications reveals a pronounced shift toward interdisciplinary fractional modeling, with significant contributions to water pollution management systems, chemical reactor design, and advanced thermoelastic theories. His methodology consistently emphasizes convergence analysis and validation against physical phenomena, establishing robust frameworks for engineering applications. Aydınlık actively leads three research projects as Principal Investigator: Düzgün Kompozit Sözde Spektral Yönteminin Bazı Mekanik Problemlerine Uygulamalı Analizi (Active: Feb 2025 - Feb 2026) applying spectral methods to mechanical systems Kesirli Genelleştirilmiş Termoelastisite Çerçevesinde 3 Boyutlu Plak Dinamiği (Active: Jul 2024 - Jan 2025) developing space-fractional thermoelastic plate models Manyetohemodinamik akış probleminin sayısal analizi (Completed: Sep 2023 - Sep 2024) analyzing magnetohydrodynamic flows

Elbrus JafarovView profile

Professor

• Sliding Mode Control
• Controller Design
• Parameter Perturbation
• +5 more

Elbrus Jafarov serves as a Professor in the Department of Aeronautical Engineering at Istanbul Technical University's College of Aeronautics. His research focuses on advanced control systems with applications in aerospace engineering, particularly in sliding mode control methodologies. Research interests include: Sliding Mode Control and Controller Design Stability Analysis of Delayed Systems Spacecraft Attitude Control under Magnetic Actuation Turbojet Engine Dynamics and Windmilling Phenomena Robust Control for Time-Varying Systems His recent publications demonstrate significant contributions to control theory, with emphasis on practical aerospace applications. Key trends include development of sliding mode controllers for input-delayed systems, stability analysis of turbojet engines during critical phases, and magnetic-based attitude control for small satellites. Scientific recognition: h-index of 11 with 635 citations (Scopus) 64 research outputs spanning 1978-2022 Active research includes the project 'Salt Manyetik Eyleme ile Küçük Uydu Yönelme Kontrolü' (Small Satellite Attitude Control via Magnetic Actuation) funded from 2013-2017, demonstrating ongoing contributions to spacecraft control systems. His work bridges theoretical control methodologies with practical aerospace implementations across multiple engineering domains.

• Control Systems
• Robotics
• Machine Learning
• +3 more

Shuai Shao is a Researcher in the Department of Internal Medicine at Yale School of Medicine. His work focuses on control systems, robotics, and machine learning with applications in distributed optimization, autonomous systems, and resilient networks. He has contributed to advancements in multi-agent coordination, inverse optimal control, and energy management systems. His email is s.shao@yale.edu . Research interests span distributed algorithms, nonlinear control, and deep learning for complex systems. Notable work includes developing resilient consensus protocols, spacecraft trajectory planning, and battery thermal management solutions. Recent publications emphasize integration of control theory with machine learning frameworks. His article trends highlight innovation in edge agreement algorithms, multi-drone cooperative systems, and Koopman-based learning for nonlinear dynamics. These contributions address challenges in safety-critical autonomous systems and energy optimization. No scientific awards are explicitly mentioned. Advising activity and grant details are currently unavailable. His research aligns with Yale’s focus on interdisciplinary solutions for healthcare and engineering problems.

Ron WeissView profile

Professor

• Synthetic Biology
• Cancer Biology
• Computational Systems Biology
• +4 more

Ron Weiss is a Professor of Biological Engineering at the Massachusetts Institute of Technology (MIT), with a joint appointment in the Department of Electrical Engineering and Computer Science (EECS). He leads the Weiss Laboratory, which focuses on synthetic biology, engineering programmable biological systems, and elucidating design principles for complex cellular phenotypes. Education: Dr. Weiss holds a PhD from MIT, where he pioneered synthetic biology research in the late 1990s. He later joined Princeton University before returning to MIT in a tenured role. Research Interests: The lab integrates engineering principles with biology to design synthetic gene networks, including analog/digital logic circuits, cell-cell communication systems, and programmable cell behaviors. Key areas include cancer biology, tissue engineering, and RNA-based therapies. Recent work includes programmable microRNA sensors and synthetic neuromorphic computing in cells. Articles Trends: His research spans programmable gene circuits, RNA engineering, and interdisciplinary applications like biohybrid robotics. Recent publications highlight advancements in cell-state transition control, LNP-RNA therapies, and synthetic genetic circuits for reprogramming cells. Advising & Grants: Dr. Weiss has advised over 20 graduate students and postdocs, many of whom have gone on to leadership roles in academia and industry. His lab collaborates with the Engineering Biology Research Consortium (EBRC) and has secured funding for projects like Space Health Roadmap and synthetic biology therapies. Labs/Teams: The Weiss Lab is part of MIT’s Synthetic Biology Center and collaborates across disciplines, including robotics and nanotechnology.

Lingbo ChengView profile

Lecturer

• Control Engineering
• Medical Robotics
• Teleoperation Control
• +5 more

Dr. Lingbo Cheng is a Lecturer at the School of Engineering, University of Newcastle, Australia, specializing in Control and Automation for Medical Robotics. Her research focuses on haptics, teleoperation control, non-linear systems, medical imaging, and multi-robot systems. Previously, she held positions as Assistant Professor at Zhejiang University and Postdoctoral Fellow at the University of Alberta. She received her PhD in Control Systems from the University of Alberta in 2019, with qualifications spanning machine learning, signal processing, and medical robotics. Her professional experience includes developing telerobotic systems for beating-heart surgery and haptics-enabled surgical training platforms. Dr. Cheng's research bridges control engineering and medical devices, emphasizing applications in surgical robotics, autonomous navigation, and human-machine interfaces. Her publications demonstrate consistent focus on reinforcement learning for autonomous systems, medical robotics control architectures, and computational methods for real-time applications. Analyzing her recent articles reveals strong trends in reinforcement learning applications for autonomous vehicles/vessels (5 papers), medical robotics innovation (4 papers), and control system optimization (4 papers), with emerging work in maritime navigation and air traffic management. Currently leading projects on teleoperation systems and multi-robot surgical training, Dr. Cheng collaborates internationally on magnetically actuated surgical tools and autonomous navigation systems. Her lab focuses on human-robot interaction for medical and transportation applications.

Florian DörflerView profile

Professor

• Distributed Control Systems
• Optimization
• Cyber-Physical Systems
• +7 more

Florian Dörfler is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and Deputy Head of the Automatic Control Laboratory. He holds a Ph.D. in Mechanical Engineering from the University of California, Santa Barbara (2013), and a Diplom in Engineering Cybernetics from the University of Stuttgart (2008). Prior to ETH, he was an Assistant Professor at UCLA (2013–2014). His research focuses on distributed control and optimization in complex systems, including smart grids, robotic coordination, and social networks. Key areas include power grid stability, online feedback optimization, and data-driven control. He has advised students who received or were finalists for prestigious awards at major conferences such as the European Control Conference and American Control Conference. Notable awards include the IFAC Manfred Thoma Medal (2020), European Control Award (2020), and IEEE Circuits and Systems Best Paper Award (2016). His work spans theoretical advancements and practical applications, with contributions to stability analysis, decentralized control, and energy systems. Led the Automatic Control Laboratory and contributes to ETH Zurich’s broader efforts in cyber-physical systems. Active in academic service, including editorial roles and conference organization. His lab develops cutting-edge solutions for grid resilience, distributed optimization, and networked systems.

Bernard BayleView profile

Professor

• Robotics
• Medical Robotics
• Human-Robot Interaction
• +2 more

Bernard Bayle is a Professor at the University of Strasbourg and a member of the ICube Laboratory within the Robotics, Data Science, and Healthcare Technologies research group, which he headed from 2018 to 2023. He leads the HealthTech Interdisciplinary Institute that combines an interdisciplinary research cluster with a graduate school. His research focuses on the design, modeling, and control of robotic systems, particularly for healthcare applications including physical human-robot interactions and robot-assisted medical interventions. He teaches courses in Mechatronics and Haptics, Mobile Robotics, and Robot-Assisted Interventions at Télécom Physique Strasbourg. His research has resulted in over 100 scientific publications, 33 journal papers in top robotics and engineering venues, and 7 patent applications. His research primarily explores robotics applications in medical contexts, including surgical assistance, rehabilitation technologies, and diagnostic systems. Recent work focuses on haptic feedback systems, continuum robots, and autonomous medical intervention platforms. He has supervised 19 PhD students and led 5 research projects as principal investigator while participating in 11 others. His lab focuses on developing robotic solutions for healthcare challenges through interdisciplinary collaboration.

Bohong ZhangView profile

Academic

• Optical Sensing
• Fiber Optic Sensors
• Biomedical Sensing
• +3 more

Dr. Bohong Zhang is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. His research focuses on developing advanced optical and microwave sensors for harsh environments, biomedical applications, and intelligent infrastructure systems. He holds a Ph.D. in Electrical Engineering from Missouri S&T. Education: PhD in Electrical Engineering (Missouri S&T) Key Research Areas: Fiber Optic Sensors, Optical Spectroscopy, Ultrafast Laser Processing, Material Characterization His work integrates cutting-edge techniques like femtosecond laser fabrication and machine learning to create robust sensors for industrial processes (steel manufacturing, cement hydration), biomedical diagnostics, and environmental monitoring. Recent projects include real-time mold flux analysis in steel production and pathogen detection using Raman spectroscopy-based systems. Awards: 2022: 2nd Place IEEE St. Louis Student Presentations 2022: 3rd Place Missouri S&T Three Minutes Poster Competition 2021: CEC Dean’s Graduate Educator Award Research Highlights: Pioneering distributed fiber optic sensor networks for industrial monitoring, developing miniaturized wearable biosensors, and advancing SERS-based chemical detection systems. Current projects emphasize sensor durability in extreme conditions and AI-driven data interpretation.

Xingjie LiView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Mathematical Physics
• +3 more

Xingjie (Helen) Li is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. Her research focuses on numerical analysis, partial differential equations, and mathematical physics with an emphasis on nonlocal models, multiscale methods, and computational mechanics. She develops advanced numerical schemes for stochastic systems and explores applications in materials science, image processing, and epidemiological modeling. Her work bridges theoretical analysis and practical computation, addressing challenges in coarse-graining stochastic dynamics, atomistic-to-continuum coupling, and probabilistic collocation methods for heterogeneous materials. Recent studies include innovative approaches to nonlocal diffusion problems, peridynamic fracture modeling, and data-driven time-stepping algorithms for ergodic systems. She has also applied mathematical techniques to analyze real-world scenarios such as pandemic dynamics in Singapore and Japan. Key contributions include energy-preserving finite difference schemes, feature-oriented imaging compression frameworks, and inference-based adaptive methods. Her research portfolio reflects interdisciplinary collaboration spanning computational physics, applied mathematics, and data science.

Ville HautamäkiView profile

Associate Professor

• Data Science
• Statistical Inference
• Deep Learning
• +3 more

Ville Hautamäki is an Associate Professor at the University of Eastern Finland's School of Computing, Department of Computer Science. His research focuses on data science, statistical inference, and deep learning with applications in autonomous agents, bioinformatics, and speech technology. He teaches courses such as Probabilistic Inference for Data Science and Bayesian Inference, and regularly contributes to summer schools on machine learning. His research group, Applied Statistics and Statistical Machine Learning, addresses challenges in speaker verification, speech processing, and biomedical data analysis. Recent works include advancements in robust speaker recognition under noisy conditions, deepfake detection, and end-to-end autonomous driving systems. Collaborations span diverse domains including healthcare, cybersecurity, and robotics. Publications highlight contributions to multi-task learning frameworks, imitation learning policies, and generative models for single-cell data. His work emphasizes cross-disciplinary approaches, blending theoretical machine learning with practical applications in real-world scenarios.